HEALTHCARE AND LIFE SCIENCES
Predictive analytics and machine learning are rapidly gaining traction in the healthcare and life sciences industry. Healthcare can be made more proactive through the utility of predictive analytics for improving patient care, prolonged disease management, hospital administration, and to overcome supply chain inefficiencies. The industry is lately getting overloaded with data, majorly from the patient, clinical, claim, hospital system, financial, pharmacy, and from wearable technology sources. The industry is pushing towards generating electronic health records and periodically updating reporting methods and data storage with the advent of advanced analytical technologies for decision-making.
In healthcare, the prediction is most useful when gathered insights can be utilized into action. Hence, with the on-going development of predictive analytics software, healthcare providers are adopting the predictive analytics solutions. According to a survey carried by Society of Actuaries (SOA), a professional organization for actuaries based in North America, around 47% of the providers use predictive analytics.
Additionally, owing to the worldwide adoption of electronic health records, large healthcare institutions, and healthcare systems have begun to recognize analytics as mean to predict future and has relied on the capabilities to bring patients trends and patterns. The real-time EHR data analytics helped a hospital located in Texas, US to cut the patient readmission by 5%, indicating the importance of real-time analytics in the healthcare industry. Predictive analytics solutions enable organizations to consolidate data at a centralized location, categorize it, and maintain it in an easy way to understand format resulting in enhanced customer experience.
Moreover, the Veteran’s Health Administration (VHA) accumulated over 30 years of electronic patient data, which later, post building a data warehouse and developing predictive algorithms that are able to predict health and death risks, it used this data to enhance its efficiency while improving the quality of patient care. This lead VHA to receive a net benefit of USD 3 billion through predictive analytics.
COMPETITIVE LEADERSHIP MAPPING TERMINOLOGY
The Healthcare and Life Sciences predictive analytics software vendors are placed into 4 categories based on their performance in each criterion: “visionary leaders,” “innovators,” “dynamic differentiators,” and “emerging companies.” The top 23 vendors evaluated in the data quality tools market include Agilone, Alteryx, Inc, Angoss Software Corporation, Dataiku, Domino, Data Lab, Exago, Inc., Fair Isaac Corporation (Fico), Good Data, IBM Corporation, Information Builders, Inc., Knime Ag, Microsoft Corporation, Microstrategy Incorporated, NTT Data Corporation, Oracle Corporation, Qliktech, Inc., Rapidminer, Inc, SAP SE, SAS Institute Inc, Sisense, Tableau Software Inc, Teradata Corporation, and Tibco Software Inc.
Use Cases of Predictive Analytics software in Healthcare and Life Sciences
- Risk Scoring for Chronic Diseases, Population Health: Creating risk scores based on lab testing, biometric data, claims data, patient-generated health data and the social determinants of health provide healthcare providers insight into individuals so that they benefit from enhanced services
- Getting Ahead of Patient Deterioration: Data analytics can help providers react as quickly as possible to changes in a patient’s vitals and able to identify an upcoming deterioration before symptoms are clearly visible to the naked eye
- Preventing Suicide and Patient Self-Harm: EHRs can support suicide risk detection using a predictive algorithm
- Predicting Patient Utilization Patterns: Using analytics to predict patterns in utilization can help ensure optimal staffing levels while reducing waiting times. Visualization tools and analytics can model patient flow patterns and highlight opportunities to make workflow adjustments or changes
- Supply Chain Management: Predictive tools can help hospital executives gain more actionable insights into ordering patterns and supply utilization
Developing Precision Medicine and New Therapies: Predictive analytics and clinical decision support tools are used in translating new drugs into precision therapies. Tools are able to predict a patient’s response to a certain course of treatment by matching genetic information with the results from previous patient cohorts, allowing providers to choose the most likelihood therapy
Case Studies of Predictive Analytics Software in Healthcare and Life Science
Case Stuy: Advocate Health Care lowers readmissions with Cerner’s predictive analytics solution
Advocate Health Care partnered with Cerner and created Advocate Cerner Collaborative (ACC) in 2012. Since then both organizations have worked together to develop advanced, evidence-based analytics to improve the quality of patient care
- Patients who received high risk education using Cerner solutions had a 20% lower readmission rate
Case Study: At Bellin Health, Epic system’s teamwork was used for patient care. When a patient’s physician asks about his/her health, a nurse or other member of the care team uses Teamwork to quickly review and address all the care related gaps
- Bellin’s comprehensive approach to each patient’s care helped keep its patients healthy due to which 75% of all visits at Bellin Health in 2018 were regular wellness visits
- This focus on wellness helped Bellin Health achieve top performance in the Next Generation ACO program
Clarify Health Solutions:
Case Study: John Muir Health, located in San Francisco Bay Area, was struggling to manage value-based programs in the dark, without the evidence to inform and support decisions.
Clarify’s solution provides the health system with the critical analytical foundation for their bundled payments program. It delivers the actionable insights needed to succeed in the CJR program.
- John Muir Health’s system scored an excellent rating on quality
- Captured USD 1,900 per episode in bonus in the first year of the CJR program
Case Studuy: Natividad Medical Center, a 172-bed acute care hospital located in Salinas, California, faced issues regarding Data transparency and Real-time data visualization.
To help frontline care staff prioritize their concurrent tasks, Qventus gave nurses actionable nudge to focus on actions that would directly impact patient flow. The system is able to predict issues before they occur and prescribe actions which should be immediately taken to go ahead
- Average LWBS rate dropped 42%, shedding 1.6 percentage points using 18 months of data
- Average admitted patients LOS dropped 30 minutes, an 8% reduction
- Door-to-doc time shortened by 10 minutes, a 20% reduction
- Provide an estimated 850 additional visits yearly with a projected $425,000 in additional revenue
Case Study: Health Quest, a non-profit, four-hospital health system with locations in Connecticut and New York’s Hudson Valley, needed access to real-time patient data to meet quality-based performance benchmarks.
Health Quest was able to identify gaps, track individual touch points and refine its care process to improve system-wide population health
- Health Quest generated USD 3.7 million in total billing revenue
- Received a final MIPS score of 93.32 out of 100 resulting in a 1.65 percent payment bonus in year 1 and met the care management requirements of CPC+ Track 2
Case Study: Leidos and the University of Miami Health System have worked together on a predictive analytics solution that can deliver data to physicians wherever they need it, enabling them to make informed decisions at the point of care. The doctor can see the system's recommendations on the screen and can take immediate action during the consultation.
- Elaborated a potential cost saving of up to USD 12.5 million as pre-diabetic patients are successfully identified and put through diabetes prevention training
Case Studies:Fraser Health Authority, Canada is using AllScripts’ DbMotion clinical analytics which enables clinicians to examine wider caseloads than they have in their own practices and making information available within the clinician workflow
- It helped Fraser Health standardize processes ahead of time and prepare with extensive testing, particularly for data validation before going live
Case Study: Employer Health Plan successfully lowers costs and boosts benefits. Health Catalyst decided to embrace self-insurance to take the management of its healthcare costs and benefit design into its own hands as well as gain access to the data it needed to manage its population health.
The organization is currently leveraging data and analytics to help uncover insights into improvement opportunities and methods to drive behavior change in its team member
- Successfully moved from a unmanaged organization to a self-insured/managed organization in less than five years
- Re-invested cost savings into enhancing employee benefits
Case Study: Predicting High-risk Chronic Kidney Disease (CKD) Patients. Medictiv team of CitiusTech identified key analytics features required by nephrologists, built a technological roadmap for analytics, implemented data cleansing, transformation and quality checks to build data confidence
- Developed and validated clinical models with accuracy upto 74%
- Developed real-time score cards to track data quality, cleanse and profile data for different use cases
Case Study: Inovalon deployed a strategy that included direct mail, telephonic communications and appointment reminders via SMS text message to the payer’s commercial patients which were identified as possible care gaps.
The goal was to improve overall health outcomes for the patient population while driving efficiencies and improving financial performance through multiple channels
- National Healthcare payer increases patient intervention completion rate by 73% with 55% fewer program eligible patients
Case Study: Biopharma Companies’ Real-time data were collected daily from community oncology practices across the country representing thousands of physicians.
To successfully introduce new therapies and support long-term commercial needs, biopharma companies required a deep understanding of disease landscapes so that they must be able to quickly identify the patient population, understand patterns of care and develop a plan to deliver appropriate clinical education and messaging to physicians in order to help them make the most favorable clinical decisions for their patients
- McKesson’s comprehensive data analytics model collected structured clinical data from more than 2,200 providers and 650 sites of care across the U.S and took timely actions
Case Study: Adventist Health, a non-profit healthcare provider based in California, used MedeAnalytics Patient Access across its 19 hospitals to increase point-of-service collections, patient experience, and streamline patient registration workflows
- Adventist Health boosted point-of-service collections by $3.8 million over two years, representing a 20% increase across the organization
Case Study: Randomized trial in Oregon showed that expanding Medicaid coverage increased emergency department (ED) use by 40% including visits for conditions that might best be treated by a primary care physician.
NextNudge, which uses machine-learning techniques, was used to identify members to nudge such as relatively healthy members with no recent wellness visits but with a history of recent ED use and to track the results of nudging them
- 25% Reduction in Avoidable Emergency Room Visits
Case Study: A Health informatics company required a sophisticated and flexible system with a Business Intelligence dash board and reporting solution having Data Visualization features which enables access to real-time actionable information on spends and performance
- Better visibility on spends to optimize the procedure and reduce cost
- Improved decision making on health insurance plans with reduced assumptions
Case Study: Flatiron Health is using data and analytics to tackle cancer. Flatiron’s goal is to accelerate cancer research and improve patient care by enabling cancer researchers and care providers to leverage its analytics software platform and learn from the experience of each patient to enhance the development of new treatments
- Flatiron platform helped in identifying right patient cohorts for almost 15 types of cancer conditions for clinical trials, thereby optimizing clinical trial enrollment
Case Study: Competitive Health Analytics (CHA), a Humana company that provides research and analytics services to the pharmaceutical and health care industries used SAS Health Analytics to perform comparative effectiveness studies, drug safety analysis and subgroup analysis to find drugs that work particularly well in certain types of patients
- Using SAS, Competitive Health Analytics grew business by 50% in one year
Case Study: Amitech worked with a Healthcare provider to develop a breakthrough mHealth platform that leverages near real-time streaming data, psychographic user profiles and a predictive analytics engine to offer users important insights and personalized nudge suggestions
- Participants in an initial pilot program increased activity by 11% and total hours slept by 17%
- First-gen platform was able to reduce claims within 6 weeks of implementation with a USD 15 million reduction in cost of care in first year
Conifer Health Solutions:
Case Study: KentuckyOne Health Partners turned to Conifer Health Solutions to help guide its care managers for positive outcomes. Conifer Health’s Population Health Intelligence platform manages the 1,00,000 lives by capturing enrollment, claims and clinical data to stratify and identify high-risk populations
- Improved quality and patient satisfaction resulting in more than USD 27 million in Medicare shared savings over the past three years
Case Study: Using Optum One, data and analytics platform, UMass combined patient information from two separate electronic medical records. Data was blended with historic patient care registry information and claims data from five payers to identify gaps in adult immunizations for flu and pneumonia
- Increased pneumonia immunization rates for Medicare patients by 17.4 percent
- Improved communication with physicians using transparent data and analytic sharing
Case Study: Rutgers Ernest Mario School of Pharmacy used Aetion Evidence Platform for collaborative, transformative generation of essential evidence at scale. Analytics platform addressed the growing need for timely, consistent and reproducible real-world evidence where data can be obtained from any sources
- Fully causal, risk-adjusted assessments
- Real-time collaboration among parties
- “Time to evidence” reduced to near real-time
Case Study: A global life sciences company was trying to get a new therapy for multiple sclerosis (MS). The therapy was in development for use in an autoimmune disorder but fell short in clinical trials.
Zephyr Health was brought in to investigate the problem and check why providers were not relevant and what, if any, changes could be made to improve the output
- Zephyr Health’s solution reduced the number of irrelevant key opinion leaders from 51% to under 20%
- Medical Science Liaisons team was able to operate 10% faster
Case Study: Stanford Biodesign was searching for a technology platform that can enable companies to accurately quantify the value of their health-related technologies outside of clinic walls.
Evidation Health demonstrated their value and product market fit in such condition
- Evidation received a DARPA grant to execute a virtual RCT in over 75,000 patients with the goal of understanding how mobile-based interventions could impact US flu vaccination rates
Case Study: Northwest Primary Care involved in value-based care through Medicare Advantage plans. To meet clinical and financial targets, Northwest Primary Care relied on a care team that is integrated through technology and manages patients using strategies that mitigate clinical risk
- Improved Care coordination to minimize hospitalizations
- Provided proactive approach to value-based care
Case Study: Healthcare Access San Antonio (HASA), the non-profit community, needed enhancements in its master patient index (MPI) capability and comprehensive reporting efforts. It also required a more robust data warehousing solution and the ability to identify gaps in both unrecognized and unleveraged data
- Allowed HASA to liberate the data for smaller providers who now have a full picture of their patients through the IMAT platform
- Reduced penalties for not meeting the 30-day risk standardized readmission measures
Case Study: Chinook Health, part of Alberta Health Services, was facing issues in the pending retirement of their MAGIC system. Several options were considered to preserve the historical MAGIC data, but did not have any of the non-converted historical data. Acmeware helped in finding solution here
- Unavailable historical data from a soon-to-be retired MAGIC system allowed the preservation of patient data to present a complete clinical history
Predictive Analytics Software in Healthcare and Life Sciences Quadrant
Find the best Predictive Analytics Software solution for your business, using ratings and reviews from buyers, analysts, vendors and industry experts
- Product Quality and Reliability
- Support for Custom Data Connectors
- Custom Scripting Language
- Deployment Type
- Hybrid (Deployment type)
- Target Users
- Database Administrators
- Business Analysts
- Data Scientists
- Non Technical Users
- Application Developers
- Support for Languages
- Support for R
- Delivery Mode
- Separate Platform
- As a Service / Connector Free
- Add-on Funtionalities
- Machine Learning / AI
- Streaming / Real-Time
- Mobile Support / Mobile BI
- Product Features and Functionality
- Integration with Big Data Frameworks / Data Stores
- Apache Spark
- Enterprise Features
- Analytics Workflow
- Shared Data Sources
- Server Side Data Processing
- Cloud Hosted Data
- Licensing - Data Volume
- Costs & Units
- Cost - $ per license
- Hybrid (Please specify)
- Core Features
- Visual Analytics Design / Code Free
- Data Investigation
- Statistical Modelling
- Times Series Exploration
- Root Cause Analysis
- Advanced Condition Prediction
- Predictive Grouping
- No. of Third Party Data Providers
- Natural Language Processing (NLP)
- Event Detection
- Breadth and Depth of Product Offering
- Data Management
- Data Preparation (Data Management)
- Interactive Visualisation
- Real Time Dashboarding
- Static Visualisation
- Report Generation
- Data Blending
- Report Automation
- Data Collections
- Customer Data
- Transaction Data
- Geo Spatial
- Location [Pincodes]
- Marketing Data
- Use Cases
- Business Intelligence
- Data Visualisation
- Customer Response Modelling
- Demand Forecasting
- Data Preparation
- Operations Management
- Fraud Detection & Prevention
- Pricing Elasticity Analysis
- Location Intelligence
- Risk Management
- Customer Data Platform
- Sales and Marketing Management
- Network Management
- Workforce Management
- Supply Chain Management
- Web and Social Media Management
- Financial Management
- Root Cause Analysis (Use case)
- Predictive Maintenance and Asset Management
- Event Detection (Use case)
- Services Offered
- Support and Maintenance
- Custom Predictive Algorithms
- Requirement Definition
- Managed Services
- Report Authoring
“Decision making made easier with this data analysis program."
The software’s ability to organize and use variable for tool application is what works best for me. Evaluation of the behavior of dependent and independent variables for linear regression analysis makes it easy to compile reports, further enabling easier decision making. It is also extremely user-friendly, with each icon distinctly visible. If I had to pick an area of improvement, I would say it is the quality of its graphics. They do not seem very professional and perhaps they can be updated to seem so.
I believe that this is an ideal software for organizations lookimg to systemize its data and work using dependent as well as independent variables. It works excellently to present inferential statistics to help organizations grow. However, if you’re looking for exceptional graphics, then this might not be the one for you.
“Navigate through Journey of Hypotheses"
“Efficient and Dependable"
This is a highly dependable software that also has some great features. Some of these features include
- Easy access to data.
- Presents data in the required format, after using various metrics.
- Creates good dashboards.
“A great way to professionally build and manage databases"
“One of the best software in the market"
“Worth the price"
“World class visualization"
“Data Science Capabilities Without The Investment In Data Scientists"
“Very easy, very accurate"
“Easy to interpret data"
“Good Product with Some Bad Tooling Options"
“Azure’s Big Data and Interactive Dashboards Truly Excel"
“Capable Software with Good Visual Appeal"
“Easy and rapid extraction of insights"
“Good analytic data platforms"
“Cloud based predictive Analytics"
The product team met with us at the time of installation which helped the entire process move smoothly. Even after installation the team continued to provide exceptional service. Product met all expectations with only minor issues. Which is a good thing considering they have horrible documentation. It’s all over the place and can do with a major overhaul. The stellar support not only makes up for the lack of documentation, but also makes this a product to recommend.